System and method for providing photo-based estimation

ABSTRACT

Methods and systems for facilitating photo-based estimation are described. In an aspect, a server is configured to send via a communications module to a remote computing device a first signal comprising a chat interface. The server may receive, via the communications module and from the remote computing device, a second signal representing input received at the remote computing device through the chat interface. The server may identify an account associated with the remote computing device and retrieve policy data associated with the identified account from the data store. The server may automatically evaluate the input and policy data against predetermined criteria to determine whether a claim has a low risk level and, when the claim is determined to have a low risk level, engage a photo-based estimation module.

TECHNICAL FIELD

The present application relates to automated processing of claims and,more particularly, to systems and methods for use with photo-based claimprocessing.

BACKGROUND

When a party wishes to make an insurance claim, that party may contactan insurer by telephone and the insurer may assign a claims adjuster tothe claim. The claims adjuster evaluates the claim. In order to evaluatethe claim, the claims adjuster may inspect property, such as a vehicle,that is associated with the claim. The claim adjustment process may takea considerable amount of time and effort.

Computers have sometimes been used to improve the claims process. Forexample, some insurers may provide a web interface that allows forsubmission of an online claim form. Typically, online claim submissionsare also routed to a claims adjuster. This claim adjustment process mayalso be time consuming and result in a large delay prior to processingof a claim.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments are described in detail below, with reference to thefollowing drawings:

FIG. 1 is a schematic operation diagram illustrating an operatingenvironment of an example embodiment;

FIG. 2 is a simplified schematic diagram showing components of acomputing device;

FIG. 3 is high-level schematic diagram of an example computing device;

FIG. 4 shows a simplified organization of software components stored ina memory of the example computing device of FIG. 3;

FIG. 5 is a flowchart showing operations performed by a server inperforming photo-based estimation for a claim;

FIG. 6 is a flowchart showing operations performed by a server to routeprocessing of a claim;

FIG. 7 is a flowchart showing operations performed by a server toremotely determine a location of damage to a vehicle;

FIG. 8 is a flowchart showing operations performed by a remote computingdevice to enable capture of an image;

FIG. 9 is a flowchart showing operations performed by a server inproviding photo-based estimation;

FIG. 10 is a flowchart showing operations performed by a server inevaluating image data;

FIG. 11 is an example screen of a graphical user interface;

FIG. 12 is a further example screen of a graphical user interface;

FIG. 13 is yet a further example screen of a graphical user interface;and

FIG. 14 is yet a further example screen of a graphical user interface.

Like reference numerals are used in the drawings to denote like elementsand features.

DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS

According to the subject matter of the present application, there may beprovided a server. The server may include a communications module and adata store storing profiles for parties. The server may include aprocessor coupled to the communications module and the data store and amemory coupled to the processor. The memory may storeprocessor-executable instructions which, when executed by the processor,configure the processor to: receive, via the communications module andfrom a remote computing device, a signal representing identificationdata; identify, based on the identification data, one or more of theprofiles stored in the data store; obtain a three-dimensional vehiclemodel based on the identified profile; send, via the communicationsmodule to the remote computing device, a second signal representingdisplay data, the display data including the three-dimensional vehiclemodel and a damage location indicator overlaid on the three-dimensionalvehicle model, the damage location indicator selectable to input anindication of a damage location; and receive via the communicationsmodule, after selection of the damage location indicator on the remotecomputing device, a third signal comprising an indicator of the locationof damage.

According to a further aspect, there is provided a method. The methodmay include: receiving, via a communications module and from a remotecomputing device, a signal representing identification data;identifying, based on the identification data, one or more profilesstored in a data store; obtaining a three-dimensional vehicle modelbased on the identified profile; sending, via the communications moduleto the remote computing device, a second signal representing displaydata, the display data including the three-dimensional vehicle model anda damage location indicator overlaid on the three-dimensional vehiclemodel, the damage location indicator selectable to input an indicationof a damage location; and receiving, via the communications module,after selection of the damage location indicator on the remote computingdevice, a third signal comprising an indicator of the location ofdamage. The method may be performed by a computing device.

In a further aspect, there is provided a non-transitory computerreadable storage medium comprising computer-executable instructionswhich, when executed, configure a computing device to: receive, via acommunications module and from a remote computing device, a signalrepresenting identification data; identify, based on the identificationdata, one or more profiles stored in a data store; obtain athree-dimensional vehicle model based on the identified profile; send,via the communications module to the remote computing device, a signalrepresenting display data, the display data including thethree-dimensional vehicle model and a damage location indicator overlaidon the three-dimensional vehicle model, the damage location indicatorselectable to input an indication of a damage location; and receive viathe communications module, after selection of the damage locationindicator on the remote computing device, a signal comprising anindicator of the location of damage.

In yet a further aspect, there is provided a server. The server mayinclude a communications module, a data store and a processor coupled tothe communications module and the data store. The server may include amemory coupled to the processor. The memory may storeprocessor-executable instructions which, when executed by the processor,configure the processor to: send via the communications module to aremote computing device a first signal comprising a chat interface;receive, via the communications module and from the remote computingdevice, a second signal representing input received at the remotecomputing device through the chat interface; identify an accountassociated with the remote computing device; retrieve policy dataassociated with the identified account from the data store;automatically evaluate the input and policy data against predeterminedcriteria to determine whether a claim has a low risk level; and when theclaim is determined to have a low risk level, engage a photo-basedestimation module, the photo-based estimation module configured toremotely receive an image captured at the remote computing device andprovide a real-time estimate based on the received image.

In yet a further aspect, there is described a method that includes:sending, via a communications module to a remote computing device, afirst signal comprising a chat interface; receiving, via thecommunications module and from the remote computing device, a secondsignal representing input received at the remote computing devicethrough the chat interface; identifying an account associated with theremote computing device; retrieving policy data associated with theidentified account from a data store; automatically evaluating the inputand policy data against predetermined criteria to determine whether aclaim has a low risk level; and when the claim is determined to have alow risk level, engaging a photo-based estimation module, thephoto-based estimation module configured to remotely receive an imagecaptured at the remote computing device and provide a real-time estimatebased on the received image. The method may be performed by a computingdevice.

In yet a further aspect, there is described a non-transitory computerreadable storage medium comprising computer-executable instructionswhich, when executed, configure a computing device to: send via acommunications module to a remote computing device a first signalcomprising a chat interface; receive, via the communications module andfrom the remote computing device, a second signal representing inputreceived at the remote computing device through the chat interface;identify an account associated with the remote computing device;retrieve policy data associated with the identified account from a datastore; automatically evaluate the input and policy data againstpredetermined criteria to determine whether a claim has a low risklevel; and when the claim is determined to have a low risk level, engagea photo-based estimation module, the photo-based estimation moduleconfigured to remotely receive an image captured at the remote computingdevice and provide a real-time estimate based on the received image.

In yet a further aspect, there is described a server that includes acommunications module. The server includes a data store storing profilesfor parties and a processor coupled to the communications module and thedata store. The server further includes a memory coupled to theprocessor. The memory stores processor-executable instructions which,when executed by the processor, configure the processor to: receive, viathe communications module from a remote computing device, a signalcomprising image data obtained by the remote computing device throughactivation of a submission application; obtain verification data, theverification data comprising at least one of policy data obtained fromat least one of the stored profiles or sensor data received from theremote computing device; evaluate the image data based on theverification data to determine whether the image data is valid; and upondetermining that the image data is not valid, generate an error.

In yet a further aspect there is described a method that includes:receiving, via a communications module from a remote computing device, asignal comprising image data obtained by the remote computing devicethrough activation of a submission application; obtaining verificationdata, the verification data comprising at least one of policy dataobtained from at least one of plurality of stored profiles or sensordata received from the remote computing device; evaluating the imagedata based on the verification data to determine whether the image datais valid; and upon determining that the image data is not valid,generating an error. The method may be implemented by a computingdevice.

In yet a further aspect, there is described a non-transitory computerreadable storage medium comprising computer-executable instructionswhich, when executed, configure a computing device to: receive, via acommunications module from a remote computing device, a signalcomprising image data obtained by the remote computing device throughactivation of a submission application; obtain verification data, theverification data comprising at least one of policy data obtained fromat least one of the stored profiles or sensor data received from theremote computing device; evaluate the image data based on theverification data to determine whether the image data is valid; and upondetermining that the image data is not valid, generate an error.

In yet a further aspect, there is described a computing device. Thecomputing device includes a camera and a communications module. Thecomputing device further includes a processor coupled to the camera andthe communications module and a memory coupled to the processor. Thememory stores processor-executable instructions which, when executed bythe processor, configure the processor to: receive, from the camera, asignal comprising image data, the image data representing at least aportion of a vehicle; retrieve data representing a preferred scene ofthe vehicle; determine, based on the image data and based on the datarepresenting the preferred scene of the vehicle, whether the image datacorresponds to the preferred scene of the vehicle; when the receivedimage data corresponds to the preferred scene of the vehicle, enablecapture of the image data; and send, via the communications module, asecond signal representing the captured image data to a processingserver configured to analyze the captured image data to assess vehiculardamage.

In yet a further aspect, there is described a method that includes:receiving, from a camera, a signal comprising image data, the image datarepresenting at least a portion of a vehicle; retrieving datarepresenting a preferred scene of the vehicle; determining, based on theimage data and based on the data representing the preferred scene of thevehicle, whether the received image data corresponds to the preferredscene of the vehicle; when the received image data corresponds to thepreferred scene of the vehicle, enabling capture of the image data; andsending, via a communications module, a second signal representing thecaptured image data to a processing server configured to analyze thecaptured image data to assess vehicular damage. The method may beperformed by a computing device. In an aspect, a non-transitory computerreadable storage medium may include processor-executable instructionswhich, when executed, configure a processor to perform the method.

In yet a further aspect, there is described a server. The serverincludes a communications module and a data store storing policy datafor a plurality of policies. The server further includes a processorcoupled to the communications module and the data store and a memorycoupled to the processor and storing processor-executable instructionswhich, when executed, configure the processor to: receive, via thecommunications module from a remote computing device, a signalrepresenting a credential; identify, based on the credential, one of theplurality of policies; determine, based on the identified one of theplurality of policies, a vehicle type of an insured vehicle; obtain,from the data store and based on the vehicle type, data representing apreferred scene of the insured vehicle; send, via the communicationsmodule and to the remote computing device, the data representing thepreferred scene of the vehicle; and receive, from the remote computingdevice, a second signal comprising image data representing at least aportion of the insured vehicle.

In yet a further aspect, there is described a method that includes:receiving via a communications module from a remote computing device, asignal representing a credential; identifying, based on the credential,one of a plurality of policies; determining, based on the identified oneof the plurality of policies, a vehicle type of an insured vehicle;obtaining, from the data store and based on the vehicle type, datarepresenting a preferred scene of the insured vehicle; sending, via thecommunications module and to the remote computing device, the datarepresenting the preferred scene of the vehicle; and receiving, from theremote computing device, a signal comprising image data representing atleast a portion of the insured vehicle. The method may be performed by acomputing device. In an aspect, a non-transitory computer readablestorage medium may include processor-executable instructions which, whenexecuted, configure a processor to perform the method.

Automated photo-based estimation techniques and may be described herein.For example, a remote computing device, such as a smartphone, may beused to submit a claim to a remote server. During the claim submission,photo-based estimation may be employed to automatically evaluate andprocess at least some claims. Photo-based estimation allows forautomated adjudication of a claim based on image data. Morespecifically, images may be algorithmically analyzed to quantify anamount of damage to insured property, such as an insured vehicle, and toautomatically determine a repair or replacement cost for the insuredproperty.

Conveniently, where photo-based estimation is employed, claim adjustmentmay be provided automatically and in real time. For example, aprocessing server may automatically evaluate one or more imagesassociated with a claim in order to quantify an amount of damage to aninsured vehicle and may then automatically propose a settlement amountbased on the amount of damage.

Other aspects and features of the present application will be understoodby those of ordinary skill in the art from a review of the followingdescription of examples in conjunction with the accompanying figures.

In the present application, the term “and/or” is intended to cover allpossible combinations and sub-combinations of the listed elements,including any one of the listed elements alone, any sub-combination, orall of the elements, and without necessarily excluding additionalelements.

In the present application, the phrase “at least one of . . . or . . . ”is intended to cover any one or more of the listed elements, includingany one of the listed elements alone, any sub-combination, or all of theelements, without necessarily excluding any additional elements, andwithout necessarily requiring all of the elements.

FIG. 1 is a schematic operation diagram illustrating an operatingenvironment of an example embodiment.

As illustrated, a server 160 and a remote computing device 100, such asa smartphone, communicate via a network 120.

The remote computing device 100 and the server 160 may be ingeographically disparate locations. Put differently, the remotecomputing device 100 may be remote from the server 160.

The remote computing device 100 and the server 160 are computer systems.The remote computing device 100 may take a variety of forms including,for example, a mobile communication device such as a smartphone, atablet computer, a wearable computer such as a head-mounted display orsmartwatch, a laptop or desktop computer, or a computing device ofanother type.

The remote computing device 100 is adapted to present a graphical userinterface that allows for remote submission of a claim to the server160. For example, the remote computing device 100 may be adapted tosubmit claim information through a chat interface that may be providedon the remote computing device 100. By way of example, the claiminformation may include one or more photos associated with the claim.The photos may represent a portion of an insured vehicle, for example.

As further described below, the server 160 is adapted to performautomated claim processing of at least some claims. For example, theserver 160 may be a claims processing server, which may also be referredto as a processing server, which is configured to selectively performphoto-based estimation. In doing so, the server 160 may receive one ormore image of at least a portion of a vehicle and may analyze the imageto automatically determine a value associated with a claim. For example,the server 160 may automatically determine an estimate of a repair orreplacement cost associated with the claim based on the image(s) and mayprovide the estimate to the remote computing device 100 for display.Operations associated with the server 160 will be described in greaterdetail below.

The network 120 is a computer network. In some embodiments, the network120 may be an internetwork such as may be formed of one or moreinterconnected computer networks. For example, the network 120 may be ormay include an Ethernet network, an asynchronous transfer mode (ATM)network, a wireless network, or the like.

As further explained below, the remote computing device 100 communicateswith the server 160 via the network 120 in order to allow for a claimsubmitted by the remote computing device 100 to be automaticallyprocessed by the server 160. That is, in at least some embodiments, theserver 160 may process the claim without any human intervention.

As illustrated in FIG. 1, the server 160 and/or the remote computingdevice 100 may also communicate with an operator device 150. Theoperator device 150 is or includes a computing device. The operatordevice 150 is a computer system.

The operator device 150 is adapted to be operated by an operator, whomay be a claims processor. In at least some embodiments, the operatordevice 150 may only be engaged when the server 160 determines thatautomated claims processing, such as photo-based estimation, is notavailable. For example, when the server 160 determines that automatedclaims processing is not available (e.g., if the risk of usingphoto-based estimation is too high, if images appear to have beentampered with, etc.), it may hand off a chat session between a chat-boton the server 160 and a user on the remote computing device 100 to theoperator device 150 so that an operator may engage in a remote chat withthe user via the network 120. When the server 160 hands off the chatsession, it may do so unbeknownst to the user. That is, the chat-bot maysimply cease operating and the operator may take over seamlessly. Tofacilitate such handoff, the server 160 may provide the operator device150 with a chat history when the chat session is handed over to theoperator device 150.

Accordingly, the operator device 150 may communicate with one or both ofthe server 160 and the remote computing device 100 via the network 120.

FIG. 2 is a simplified schematic diagram showing components of theremote computing device 100.

The remote computing device 100 may include modules including, asillustrated, for example, one or more displays 210, an image capturemodule 230, a sensor module 240, and a computing device 250.

The one or more displays 210 are a display module. The one or moredisplays 210 are used to display user interface screens that may beused, for example, to submit a claim to the server 160 (FIG. 1). The oneor more displays 210 may be internal displays of the remote computingdevice 100 (e.g., disposed within a body of the remote computingdevice).

The image capture module 230 may be or may include a camera. The imagecapture module may be used to obtain image data, such as images. As willbe described in greater detail herein, at least some such images may beused for photo-based estimation during processing of a claim. The imagecapture module 230 may be or may include a digital image sensor systemas, for example, a charge coupled device (CCD) or a complementarymetal-oxide-semiconductor (CMOS) image sensor.

The image capture module 230 and the display 210 may cooperate in atleast one operating mode of the remote computing device 100 to provide aviewfinder. The viewfinder may display camera data obtained via theimage capture module 230 in real time or near real time on the display210.

The sensor module 240 may be a sensor that generates sensor data basedon a sensed condition. By way of example, the sensor module 240 may beor include a location subsystem which generates sensor data that is alocation. The location may be the current geographic location of theremote computing device 100. The location subsystem may be or includeany one or more of a global positioning system (GPS), an inertialnavigation system (INS), a wireless (e.g., cellular) triangulationsystem, a beacon-based location system (such as a Bluetooth low energybeacon system), or a location subsystem of another type.

The computing device 250 is in communication with the one or moredisplays 210, the image capture module 230, and the sensor module 240.The computing device 250 may be or include a processor which is coupledto the one or more displays 210, the image capture module 230, and/orthe sensor module 240

FIG. 3 is a high-level operation diagram of an example computing device300. In some embodiments, the example computing device 300 may beexemplary of the computing device 250 (FIG. 2) and/or the server 160(FIG. 1) and/or the operator device 150 (FIG. 1).

The example computing device 300 includes a variety of modules. Forexample, as illustrated, the example computing device 300 may include aprocessor 310, a memory 320, a communications module 330, and/or astorage module 340. As illustrated, the foregoing example modules of theexample computing device 300 are in communication over a bus 350.

The processor 310 is a hardware processor. The processor 310 may, forexample, be one or more ARM, Intel x86, PowerPC processors or the like.

The memory 320 allows data to be stored and retrieved. The memory 320may include, for example, random access memory, read-only memory, andpersistent storage. Persistent storage may be, for example, flashmemory, a solid-state drive or the like. Read-only memory and persistentstorage are a non-transitory computer-readable storage medium. Acomputer-readable medium may be organized using a file system such asmay be administered by an operating system governing overall operationof the example computing device 300.

The communications module 330 allows the example computing device 300 tocommunicate with other computing devices and/or various communicationsnetworks. For example, the communications module 330 may allow theexample computing device 300 to send or receive communications signals.Communications signals may be sent or received according to one or moreprotocols or according to one or more standards. For example, thecommunications module 330 may allow the example computing device 300 tocommunicate via a cellular data network, such as for example, accordingto one or more standards such as, for example, Global System for MobileCommunications (GSM), Code Division Multiple Access (CDMA), EvolutionData Optimized (EVDO), Long-term Evolution (LTE) or the like.Additionally or alternatively, the communications module 330 may allowthe example computing device 300 to communicate using near-fieldcommunication (NFC), via Wi-Fi™, using Bluetooth™ or via somecombination of one or more networks or protocols. In some embodiments,all or a portion of the communications module 330 may be integrated intoa component of the example computing device 300. For example, thecommunications module may be integrated into a communications chipset.In some embodiments, the communications module 330 may be omitted suchas, for example, if sending and receiving communications is not requiredin a particular application.

The storage module 340 allows the example computing device 300 to storeand retrieve data. In some embodiments, the storage module 340 may beformed as a part of the memory 320 and/or may be used to access all or aportion of the memory 320. Additionally or alternatively, the storagemodule 340 may be used to store and retrieve data from persisted storageother than the persisted storage (if any) accessible via the memory 320.In some embodiments, the storage module 340 may be used to store andretrieve data in a database. A database may be stored in persistedstorage. Additionally or alternatively, the storage module 340 mayaccess data stored remotely such as, for example, as may be accessedusing a local area network (LAN), wide area network (WAN), personal areanetwork (PAN), and/or a storage area network (SAN). In some embodiments,the storage module 340 may access data stored remotely using thecommunications module 330. In some embodiments, the storage module 340may be omitted and its function may be performed by the memory 320and/or by the processor 310 in concert with the communications module330 such as, for example, if data is stored remotely. The storage modulemay also be referred to as a data store.

Software comprising instructions is executed by the processor 310 from acomputer-readable medium. For example, software may be loaded intorandom-access memory from persistent storage of the memory 320.Additionally or alternatively, instructions may be executed by theprocessor 310 directly from read-only memory of the memory 320.

FIG. 4 depicts a simplified organization of software components storedin the memory 320 of the example computing device 300 (FIG. 3). Asillustrated, these software components include an operating system 400and an application 410.

The operating system 400 is software. The operating system 400 allowsthe application 410 to access the processor 310 (FIG. 3), the memory320, and the communications module 330 of the example computing device300 (FIG. 3). The operating system 400 may be, for example, Google™Android™, Apple™ iOS™, UNIX™, Linux™, Microsoft™ Windows™, Apple OSX™ orthe like.

The application 410 adapts the example computing device 300, incombination with the operating system 400, to operate as a deviceperforming a particular function. For example, the application 410 maycooperate with the operating system 400 to adapt a suitable embodimentof the example computing device 300 to operate as the computing device250 (FIG. 2) of the remote computing device 100 (FIG. 1) or as theserver 160 (FIG. 1) or as the operator device 150 (FIG. 1).

Where the application 410 is provided on the remote computing device100, the application may be a submission application, which may also bereferred to as a claim submission application. The application 410 maybe a web-based or standalone application. The application 410 may beconfigured to engage in an authenticated session with the server 160.The authenticated session may occur, for example, after the remotecomputing device 100 has validly authenticated itself to the server 160using, for example, one or more credentials. During the authenticatedsession, the remote computing device 100 may engage in encryptedcommunications with the server 160. For example, as will be describedbelow, the remote computing device 100 may send image data to the server160 and the server may use the image data to automatically process aclaim (i.e., using photo-based estimation). The image data may be sentin an encrypted format. Conveniently, the encrypting of the image datamay ensure that the server 160 only receives and processes images fromauthentic submission applications. Non-authentic submission applicationscannot submit such image data since they are not able to encrypt theimage data in a format that would be recognized or accepted by theserver 160.

Where the application 410 is provided on the server 160, the application410 may include a plurality of software modules associated with claimprocessing. For example, a fraud detection module may includecomputer-executable instructions for identifying possible fraudulentclaims and/or images, a claim routing module may includecomputer-executable instructions for determining whether photo-basedestimation is to be used or whether manual estimation is to be used, aphoto-based estimation module may be used for processing a claim usingphoto-based estimation techniques, and/or a manual estimation module maybe used for engaging an operator device 150 (FIG. 1) to process a claimmanually. Other modules apart from those listed above may be includedinstead of or in addition to the modules identified and the functionsdescribed as being attributed to a specific module may instead beprovided by another module. Further, one or more of the modules may notbe included in the application 410 and/or may not be provided by theserver 160 itself. For example, the server 160 may cooperate with otherservers or computer systems which may provide some such functions.

At least some components illustrated in FIG. 4 or FIG. 5 may takedifferent forms depending on which of the computing devices they areprovided on. For example, a server 160 may include or have access to astorage module 340 (which may also be referred to as a data store) whichhas stored there profiles for a plurality of parties, who may also bereferred to as users or customers. The users may be insured persons, forexample. The profiles may be or include policy data such as insurancepolicy data. The insurance policy data may specify informationassociated with an insurance policy which may, for example, be a vehicleinsurance policy for an insured vehicle. The insurance policy data may,for example, include identification information or data for an insuredperson or an insured vehicle. By way of example, the insurance policydata for a given one of the profiles may specify vehicle identifyingdata for an insured vehicle. By way of example, the insurance policydata may specify a vehicle type of an insured vehicle. The vehicle typemay be or include one or more of an indication of whether the vehicle isa car, an indication of whether the vehicle is a truck, an indication ofwhether the vehicle is a sports utility vehicle, an indication ofwhether the vehicle is a motorcycle, a make of the vehicle (e.g., amanufacturer name), a model of the vehicle (e.g., a model name), a yearof the vehicle (e.g., a year or production or “model year” of thevehicle), a trim line indication for the vehicle (which may also bereferred to as a grade of the vehicle or trim level), an indication of apaint colour, and/or an indication of an aftermarket modification (whichmay indicate whether any aftermarket modifications have been performedon the vehicle (e.g., lowering the car, adding a spoiler, etc.).

Alternatively or additionally, the insurance policy data may specify anyone or more of: a vehicle identification number (VIN) for an insuredvehicle, a policy identifier associated with a profile (e.g., aninsurance policy number), one or more user identifiers (e.g., a name,address and/or contact information) and/or a location.

The profiles may also include other information or data about an insuredvehicle. For example, a logo (such as a manufacturer logo), a vehiclebody template, and/or a three-dimensional vehicle model may be includedin one or more of the profiles. Some such data may be used, for example,to evaluate a claim. For example, a logo displayed in an image submittedto the server 160 by the remote computing device 100 may be used todetermine whether the image is of an insured vehicle. For example, thelogo may be compared with the logo in the profile.

It will be appreciated that at least some of the information describedabove as being provided in the profile may, instead, be stored elsewhereand may be retrieved based on data in an active profile. For example,the server 160 may determine that a user is associated with a particularone of the profiles and may retrieve a year, make and model of aninsured vehicle from that profile. The server 160 may then use the year,make and/or model to determine further information about the vehicle,such as the logo, vehicle body template and/or three-dimensional vehiclemodel.

In some embodiments, the profiles may store an indication of whether acustomer is eligible for photo-based estimation. The indication may, forexample, be a flag. The flag may be considered by the server 160 whenthe server 160 determines whether a claim should be routed tophoto-based estimation or whether the claim should be routed to manualestimation.

The storage module 340 may, in some embodiments, store a set of makesand models that are or are not eligible for photo-based estimation. Forexample, a whitelist and/or blacklist of vehicle types may be stored.The whitelist may indicate vehicle types that are eligible forphoto-based estimation and the blacklist may indicate vehicle types thatare not eligible for photo-based estimation. The server 160 may use thewhitelist or blacklist in determining whether photo-based estimationshould be used to automatically process a claim.

Each profile may be associated with one or more credential that may bestored therewith. The credential may be or include any one or more of: atoken, a policy identifier, a user name, a personal identificationnumber (PIN), biometric data, and/or a password. The credential may beused by the server 160 to authenticate the remote computing device 100.More specifically, the credential may be used to determine that theremote computing device 100 is being operated by au authorized user andto identify a profile that is to be used while interacting with thatuser via the remote computing device 100 (i.e., to identify an “active”profile).

The storage module 340 may store data representing preferred scenes forvehicles for a plurality of vehicle types. The preferred scenes may eachbe associated with a vehicle type and a damage location. For example, agiven one of the preferred scenes may be associated with damage near afront left wheel for a particular make of vehicle. The data regardingthe preferred scenes may, for example, reflect a base vehicle and thedata regarding the preferred scenes may be used to aid a user incapturing an image of a vehicle that will be suitable for photo-basedestimation.

Operations performed by the remote computing device 100 and the server160 will be described below with reference to FIGS. 5-14.

The operation of the server 160, the remote computing device 100 and theoperator device 160 in facilitating claim processing will now bedescribed. Referring first to FIG. 5, a flowchart showing exampleoperations in processing a claim is described

FIG. 5 is a flowchart showing operations performed by a server inperforming photo-based estimation for a claim. The operations may beincluded in a method 600 which may be performed by the server 160. Forexample, computer-executable instructions stored in memory of the server160 may, when executed by a processor of the server 160, configure theserver 160 to perform the method 500 or a portion thereof.

At operation 510, the server 160 routes a claim to photo-basedestimation. For example, the server 160 may engage a photo-basedestimation module upon determining that a claim is an appropriate claimfor photo-based estimation.

In at least some embodiment, the server 160 is configured to supportboth photo-based estimation and manual estimation. During photo-basedestimation, photographs of an insured vehicle are evaluated to quantifythe amount of damage and to provide an estimate.

When a claim is routed to photo-based estimation, at operation 512, theserver may provide a three-dimensional vehicle model to the remotecomputing device. The server 160 may provide the three-dimensionalvehicle model during a chat session with the remote computing device100. The three-dimensional vehicle model may, for example, be rotatableand activatable.

At operation 514, the server 160 may facilitate image capture. Forexample, the server 160 may use data representing a preferred scene toenable image capture only when image data associated with an imagecapture module sufficiently corresponds to the data representing thepreferred scene. As will be described in greater detail below, in atleast some embodiments, the server 160 may only enable image capturewhen the image data sufficiently corresponds to the data representingthe preferred scene.

At operation 516, the server 160 may perform verification of capturedimage data. The verification may, for example, confirm that the imagerepresents an insured vehicle, and/or may confirm that the image was nottampered with.

After verification, the server 160 may, at operation 518, performphoto-based estimation using the image. During photo-based estimation,the server 160 may review the image to determine a location of anydamage represented in the image and may determine an estimate based onthe location of the damage and the vehicle at issue (which may bedetermined from profile data). While not illustrated in FIG. 5, ifverification fails, the server 160 may instead direct the claim tomanual estimation; for example, engage the operator device 150 (FIG. 1).

Various operations of the method 500 of FIG. 5 may include a number ofsub operations or methods and at least some of the operations will nowbe discussed in greater details.

Referring now to FIG. 6, a method 600 of routing processing of a claimis illustrated. The method 600 may be performed by the server 160. Forexample, computer-executable instructions stored in memory of the server160 may, when executed by a processor of the server 160, configure theserver 160 to perform the method 600 or a portion thereof. The method600 of routing or a portion thereof may be performed, for example, atoperation 510 of the method 500 of FIG. 5. The method 600 of FIG. 6 maybe referred to as a routing method or an automated triage method.

At operation 610, the server 160 may provide a chat interface to theremote computing device 100. That is, the server may send, via acommunications module to a remote computing device, a first signal thatcomprises a chat interface.

Referring briefly to FIG. 11, an example display screen 1100 thatincludes a chat interface is illustrated. The chat interface allows auser of the remote computing device 100 to send instant text messages tothe server 160 and the server 160 receives such messages and uses a chatbot to respond to such messages. The chat bot analyzes the messages toextract data from such messages and/or to formulate and send a reply tosuch messages. The chat bot may, for example, automatically engage in adialogue with the server 160 which attempts to obtain data regarding aclaim. For example, the chat bot may attempt to obtain answers to one ormore predetermined questions that may be used for claim routingpurposes.

The server 160 may be equipped with a natural language processing enginewhich may be used to interpret messages received from the remotecomputing device 100. In the example of FIG. 11, the server 160receives, from the remote computing device 100, a message indicatingthat a user of the remote computing device 100 was in an accident. Inresponse, the server 160 analyzes the message and determines, from themessage, that a user has a claim to submit (e.g., by determining thatthe user has been in an accident). The server 160 may then consult apredetermined question list to identify a question to send to the remotecomputing device 100. The identified question, in the example, is “areyou alright”. The server 160 may continue to receive answers toquestions and may send replies, which may be additional questions, tothe remote computing device 100. The server 160 may, therefore, engagein a chat with the remote computing device 100 which may, in someexample embodiments, appear to a user of the remote computing device toinvolve a human operator but which, in fact involves a chat bot. Inother embodiments, the remote computing device 100 may be informed thatit is engaging with a chat bot and may generate an output, such as adisplay, which informs a user of the chat-bot.

The chat-bot may, therefore, be configured to prompt a user of theremote computing device 100 to input one or more data points that may beused to automatically evaluate a claim. At operation 620, the server 160may receive, via the communications module and from the remote computingdevice, a second signal representing input received at the remotecomputing device through the chat interface.

Accordingly, the chat interface may provide a free-form input interfacewhich allows for the input of information via text or speech and thechat bot may perform natural language processing to identify relevantinformation.

The server 160 may also receive a credential for a user at operation614. The credential may be one or more of: a token, a PIN, a policyidentifier, a user name, biometric data, and/or a password. Thecredential may be received within the chat interface or outside of thechat interface. In an example in which the credential is received withinthe chat interface, the chat may prompt a user to provide identifyinginformation such as a name, address, contact information or policynumber. In an example in which the credential is received outside of thechat interface, a submission application on the remote computing devicemay provide a token to the server 160 or a password to the server.

The server 160 may, at operation 616, identify an account associatedwith the remote computing device. For example, the server may identifyone of the profiles in a storage module based on the credential(s). Oncethe credential(s) are verified as corresponding to a profile, anauthenticated session may be said to have begun.

While not specifically illustrated in FIG. 11, the server 160 mayprocess received input to determine a reply to the received input andmay send the reply to the remote computing device 100 via thecommunications module. The server 160 may then receive further input,which may be further processed.

Referring again to FIG. 6, at operation 618, the server 160 retrievesinsurance policy data associated with the identified account/profilefrom the storage module (which may also be referred to as a data store).Example insurance policy data is described in greater detail above.

In at least some embodiments, the server 160 may also obtain sensor datafrom the remote computing device at operation 620. The sensor data maybe data generated by a sensor module associated with the remotecomputing device 100. For example, the server 160 may receive a locationfrom the remote computing device. The location may be obtained at theremote computing device from a location subsystem.

Next, at operations 622 and 624, the server 160 may automaticallyevaluate the input received at operation 612 and the policy dataobtained at operation 618 against predetermined criteria to determinewhether a claim has a low risk level.

In evaluating the input and the policy data, the server may consider thepotential claim scope (e.g., the potential liability to the insurer) andone or more indicators of possible fraud (such as a claims history), inorder to determine, in real-time, whether photo-based evaluation isappropriate. The evaluation process may consider information stored atthe server-side in order to make this evaluation (e.g., the make, modeland/or year of the insured vehicle, a claim history, a residentiallocation of the user, etc.) and/or may consider information receivedfrom the user about the severity of the accident (e.g., informationabout whether the vehicle is drivable, whether a police report was filedor emergency officials were involved, whether airbags were deployed,etc.).

By way of example, in evaluating the risk level, the server 160 maydetermine that a claim does not have a low risk level when a vehiclerepresented by a make or model specified in the identified profile isdetermined to be expensive to repair. This determination may be made bycomparing a make or model specified in the obtained policy data to apredefined set of makes or models stored in a data store, such as thestorage module. For example, where a make and model pair listed in anidentified profile is included in a blacklist, then the server 160 maydetermine that a risk level is high. Alternatively, if the make andmodel pair is listed in a whitelist, then the server may determine thatthe risk level is low.

By way of further example, in evaluating the risk level, the server 160may determine that the claim does not have a low risk level when theinput indicates that an air bag has been deployed. By way of yet furtherexample, the server 160 may determine that the claim does not have a lowrisk level when the input indicates that a vehicle is not drivable.

In at least some embodiments, the server 160 may also evaluate otherinformation such as a location associated with a remote computing devicebeing used by the insured party. For example, a location obtained atoperation 620 may be compared with a residential location of the insuredparty to determine if the obtained location indicates that the user istoo far from home (e.g., greater than a threshold distance, or outsideof a home city, country, etc.) or if it is near an area (which may bedetermined from a blacklist) that is known to have a high incidence offraud. If the obtained location is too far from a home location, theserver 160 may determine that the risk for fraud is too high to proceedby way of photo-based estimation. Accordingly, in at least someembodiments, evaluating the risk level may include determining that aclaim does not have a low risk level when a received location does notcorrespond to a location specified in the policy data.

In evaluating the risk level, the server may also consider a claimshistory (which may be stored in or associated with the identifiedaccount/profile). If the user has a recent claim, a claim having a valuethat exceeds a particular threshold, or an extensive claims history,then the server may determine (at operation 624) that the risk is high.

Note that, in determining whether the risk is too high, various factorsor types of risk may be considered. For example, in some instances, thefactors and/or risk may be related to fraudulent activity. That is, theserver 160 may determine that the risk is too high if indicators suggesta high level of fraudulent activity. In some instances, the factorsand/or risk may be related to the risk that a claim may be processedwith an inaccurate estimate. Factors that may suggest an inaccurateestimate may, for example, include factors such as whether the vehicleis one that is known (i.e., is on a blacklist) as having characteristicsthat make it difficult to perform photo-based estimation. For example,certain vehicles may be known to have a paint or other feature that isknown to make it difficult to obtain quality photographs and suchvehicles may be considered high risk. By way of further example, in someinstances, it may be known that an expensive part is located internallynear a certain portion of a vehicle and damage that is indicated to bein the region of that part may be difficult to estimate since externalexamination will not accurately indicate whether the part needsreplacement. In such an instance, the server 160 may determine that therisk is too high.

In at least some embodiments, if the server 160 determines (at operation624), based on any of the above information, that the risk level ishigh, then the server 160 may prevent photo-based estimation from beingengaged. Instead, the server may initiate a manual estimation procedureby engaging a manual estimation module at operation 626. The manualestimation module may then direct the claim to an operator device 150.

Accordingly, in some implementations, the manual estimation module maybe engaged by routing a chat session to a computer terminal associatedwith a human operator, such as the operator device 150 of FIG. 1. Thechat bot may hand the chat session over to the operator unbeknownst tothe insured party (or it may hand the chat session to the operator in atransparent manner so that the nature of the interaction is known to theinsured party). In other implementations, the manual estimationprocedure may be engaged by displaying a message on the insured party'sdevice instructing the insured party to contact a call center. Forexample, the manual estimation module may hand over an ongoing chatbetween the remote computing device and a chat bot provided on theserver 160 to an operator device 150 to allow an operator to take overthe chat. In other embodiments, the manual estimation module may causethe claim process to be directed to a voice-based system. For example, atelephone call may be automatically placed to connect the remotecomputing device 100 with the operator device 150.

In some implementations, machine learning may be used to train theserver 160 to detect indicators of risk. For example, the server 160 mayinclude an artificial intelligence risk detection component that may betrained using previous chat data from previous chat sessions and riskinformation that may be associated with such chat sessions.

If, based on the information available to the server 160, the claim isdetermined (at operation 624) to have a low risk, then the server 160may (at operation 628) automatically engage a photo-based estimationmodule. As described above, the photo-based estimation module isconfigured to remotely receive an image at the remote computing deviceand provide a real-time estimate based on the received image. Thereal-time estimate may be delivered to the remote computing device 100within the chat interface immediately after receipt of the image. Anexample display screen 1400 is illustrated in FIG. 14 which includes areal time estimate within a chat message sent from the server 160 to theremote computing device 100 and displayed on a display of the remotecomputing device.

The risk level may be continually assessed as the chat progresses and,if at any point the risk is determined to be too high, then the manualestimation module may be engaged. That is, operation 624 may be repeatedas further input is received via the chat interface.

In engaging the photo-based estimation module, the server 160 may send,to the remote computing device 100 via the communications module, a userinterface having a selectable option to send image data to the server.An example of one such user interface is illustrated as a display screen1300 in FIG. 13. The display screen prompts a user to capture imagedata. The remote computing device 100 may then capture image data andmay send a signal representing the image data to the server 160. Theserver 160 receives the image data and automatically analyzes the imagedata to obtain an indicator. The indicator numerically quantifies anamount of damage to the vehicle. The indicator may, for example, be anestimated replacement or repair cost. The server 160 sends, via thecommunications module to the remote computing device, a numerical valuebased on the indicator. For example, the server 160 may send thereplacement or repair cost to the remote computing device 100 where itmay be displayed.

In some embodiments, automatically analyzing the image data to obtain anindicator may include identifying a damaged component on the vehicle andperforming a lookup of the indicator in the data store based on theidentified damaged component. By way of example, the server 160 maydetermine that a bumper is damaged and requires replacement and mayperform a lookup of the replacement cost of the bumper.

The identification of damage on the vehicle may be made by analyzing animage of the vehicle. For example, image processing techniques maycompare the vehicle in the image to data representing a base vehicle(i.e., an undamaged vehicle). The data representing the base vehiclemay, for example, including a mapping of parts. For example, the datarepresenting the base vehicle may be an image of a vehicle whichincludes markers or identifiers indicating a part associated withvarious portions of the image. Upon identifying location(s) of thereceived image that do not sufficiently correspond to similar locationsof the base vehicle image, the server 160 may determine that suchlocations represent damaged components and may then identify the damagedcomponents from the mapping of parts.

As illustrated in the display screen 1400 of FIG. 14, after the server160 has received an image of a vehicle and sent the remote computingdevice 100 a numerical value, such as an estimate of a repair orreplacement or claim cost, the server 160 may display a prompt at theremote computing device 100 to request additional images. The server maythen receive a further signal representing further image data, mayautomatically analyze the further image data to update the indicator andmay send, via the communications module and to the remote computingdevice, a further numerical value based on the updated indicator. Thatis, as additional damage is reported, the estimate may be automaticallyupdated. Conveniently, the update may be performed in real-time.

The method 600 of FIG. 6 automatically routes claims by automaticallyperforming triaging. It may be noted that routing claims prior toengaging photo-based estimation may allow for storage savings andbandwidth savings since images are not sent if they are not needed. Thatis, the method 600 may offer bandwidth or storage savings as comparedwith solutions which do not pre-screen before engaging photo-basedestimation since such systems may unnecessarily consume resourcesassociated with the transfer and storage of image files that areultimately not used when it is later determined that photo-basedestimation is inappropriate.

Referring now to FIG. 7, a method 700 of remotely identifying a locationof damage is illustrated. The method 700 may be performed by the server160. For example, computer-executable instructions stored in memory ofthe server 160 may, when executed by a processor of the server 160configure the server 160 to perform the method 700 or a portion thereof.The method 700 of FIG. 7 or a portion thereof may be performed, forexample, at operation 512 of the method 500 of FIG. 5.

At operation 710, the server 160 receives, via the communications moduleand from a remote computing device, a signal representing identificationdata. The identification data may, for example, be a credential. Thecredential may be one or more of: a token, a PIN, a policy identifier(such as a policy number), a user name, biometric data and/or apassword. The credential may be received within the chat interface oroutside of the chat interface. In an example in which the credential isreceived within the chat interface, the chat may prompt a user toprovide identifying information such as a name, address, contactinformation or policy number. In an example in which the credential isreceived outside of the chat interface, a submission application on theremote computing device may provide a token to the server 160 or apassword to the server.

Where the method 700 is performed in conjunction with the method 600 ofFIG. 6, operation 710 of the method 700 and operation 614 of the method600 may be a common operation. Features that are described above withrespect to operation 614 may be performed or provided at operation 710.

Next, at operation 712, the server 160 identifies, based on theidentification data, one or more of the profiles stored in the datastore. That is, the server identifies the profile that is associatedwith the received identification data. Where the method 700 is performedin conjunction with the method 600 of FIG. 6, operation 712 of themethod 700 and operation 616 of the method 600 may be a commonoperation. Features that are described above as being performed orprovided at operation 616 may be provided at operation 712.

Next, at operation 714, the server 160 obtains a three-dimensional (3D)vehicle model based on the identified profile. The 3D vehicle model maybe obtained from a data store that stores a plurality of 3D vehiclemodels. Each 3D vehicle model may be associated with a particular typeof vehicle and, in identifying the 3D vehicle model, the server 160 mayfirst identify a vehicle type of an insured vehicle, based on theidentified profile. For example, the server may obtain, from theprofile, insured vehicle identifying data. The insured vehicleidentifying data may identify a vehicle type. The vehicle type may beone or more of: an indication of whether the vehicle is a car; anindication of whether the vehicle is a truck; an indication of whetherthe vehicle is a sports utility vehicle; an indication of whether thevehicle is a motorcycle; a make of the vehicle; a model of the vehicle;a year of the vehicle; a trim line indication for the vehicle; or anindication of an aftermarket modification. After identifying the vehicletype of the insured vehicle from the identified profile, the server 160may then retrieve the three-dimensional vehicle model from a vehiclemodel database based on the vehicle type.

In some instances, the retrieved three-dimensional vehicle model mayrepresent the same vehicle as the insured vehicle (i.e., the same year,make and model). In other embodiments, the retrieved model may be of asimilar (but not the same) vehicle to the insured vehicle. For example,the vehicle may be of a common type (e.g., an SUV where the insuredvehicle is an SUV).

In some instances, the server 160 may have access to redesign timelinedata. The redesign timeline data may, for example, indicate years when avehicle underwent a redesign (as opposed to a minor refresh which occursin most model years). That is, the redesign timeline data may definevehicle generations. A vehicle generation is represented by date rangeswhich indicate the times when the vehicle had a generally common design.When a redesign occurs, a new vehicle generation begins.

The redesign timeline data may be used by the server 160 when selectingan appropriate 3D vehicle model. For example, when selecting anappropriate 3D model to use for a given insured vehicle, the server 160may prefer to select a 3D vehicle model that is within a same vehiclegeneration as the insured vehicle. The server 160 may be configured to,for example, select a 3D vehicle model of a vehicle that is of the samevehicle generation as an insured vehicle, even if there is another 3Dvehicle model of a vehicle that has a closer model year but that is of adifferent vehicle generation. For example, if 3D vehicle models areavailable for a particular vehicle in 2012 and 2018 and an insuredvehicle is a 2017 vehicle, and if the redesign timeline data indicatesthat a redesign occurred in 2018, the server 160 may select the 2012version of the 3D vehicle model.

Next, at operation 716, the server 160 sends, via the communicationsmodule to the remote computing device, a signal representing displaydata. The display data includes the three-dimensional vehicle model anda damage location indicator overlaid on the three-dimensional vehiclemodel. The damage location indicator is selectable to input anindication of a damage location. The display data may include agraphical user interface that allows for interaction with the 3D vehiclemodel. An example of one such graphical user interface is illustrated indisplay screens 1200, 1300, 1400 of FIGS. 12 to 14 respectively. Thegraphical user interface may allow for rotation of the 3D vehicle model.For example, the vehicle may be rotated on the display by selecting anarea associated with the vehicle and moving a pointing device (such as afinger) in a direction of rotation after such selection. Touch gesturesmay, for example, be used to control an angle of rotation associatedwith the 3D vehicle model.

As illustrated in FIGS. 12 to 14, the display data sent to the remotecomputing device 100 may cause the remote computing device 100 todisplay the 3D vehicle model in a chat interface. For example, the 3Dvehicle model may be displayed as a message below a last messagedisplayed in the chat interface. The three-dimensional model may berendered using OpenGL ES, for example.

The display data sent to the remote computing device 100 includes both a3D vehicle model and a damage location indicator overlaid on the 3Dvehicle model. The damage location indicator is selectable by a user viaan input interface (such as a touchscreen associated with the remotecomputing device 100). The damage location indicator may include aplurality of selectable locations. Each selectable location may beassociated with location information defining a location. For example,the plurality of selectable locations may be or include a grid.

In some instances, the display data may include a selectable option totoggle between 2D and 3D viewing. In some embodiments, the display datamay allow a user may be to indicate a location in 2D by selecting forexample, three planes and adjusting accordingly. In such instances theremay be a selectable feature or gesture which allows the user to togglebetween the 2D and 3D views. In some instances, the views may bedisplayed in planes perpendicular to the plane being set. In someinstances, the plane being set may be highlighted on the visualinterface in the chat session for the user to interact with.

The remote computing device 100 receives the display data and displaysthe 3D vehicle model and the damage location indicator overlaid thereon.An input may then be received at the remote computing device via aninput interface. The input may be a selection of the damage locationindicator. The selection may be made, for example, using force touch(iOS) or double tap (Android) or using other gestures. After receivingthe input, the remote computing device 100 may send a signal comprisingan indicator of the location of damage to the server 160, where theindicator of the location of damage is received at operation 718. Theindicator of the location of damage may be based on location informationof a selected on of a plurality of locations specified in the damagelocation indicator. The indicator of the location of damage mayindicate, to the server, a location where a vehicle has sustaineddamage. In one example, the indicator of the location of damage mayindicate a side (e.g., left, right, front, back) associated with thedamage. In some embodiments, the indicator of the location of damage mayinclude an image that was obtained at the remote computing device. Forexample, as illustrated in FIG. 13, after input is received selecting alocation on the 3D vehicle model, the remote computing device 100 mayinitiate image capture and an image may be captured and sent to theserver 160.

The server 160 may use the indicator of the location of damage inautomatically evaluating the claim (at operation 720). For example, theserver 160 may analyze the indicator of the location of damage to obtaina damage quantum indicator. The damage quantum indicator may numericallyquantify an amount of damage to the insured vehicle. For example, thedamage quantum indicator may be an estimate of the value of a repair,replacement or claim. The server may send, via the communications moduleand to the remote computing device, a numerical value based on thedamage quantum indicator. The numerical value may, for example, be theestimate.

It may be noted that vehicle types can vary considerably and it may beconfusing to a user when a model is displayed that does not accuratelyrepresent the type of vehicle for which a user is attempting to submit aclaim. For example, if a party has insured a motorcycle but a car isdisplayed as the 3D vehicle model, the user may have difficultyinteracting with the 3D model. Conveniently, the techniques describedabove may allow for display of a customized 3D vehicle model for theuser. The customization may be performed based on data associated withthe insurance policy. For example, the insurance policy may storeinformation about the make, model, year, trim line, etc. associated withthe vehicle. As noted above, the server may identify the insured partybased on identifying information (such as a name, address, policynumber, password, token, plate number, etc.) received at the user'selectronic device and submitted to the server. The server may then usethis information to obtain the appropriate 3D vehicle model. Forexample, a database may be populated with 3D vehicle models of variousvehicles and the server may use the model that most closely representsthe insured vehicle, thus allowing for ease of interaction with the 3Dvehicle models. In some embodiments, a user may augment their vehicleand, in such situations, the 3D vehicle model may be selected based onthe modification or may be augmented based on the modification. Themodification may be identified by the server based on an indication ofan aftermarket modification stored in the identified profile.

While not illustrated in FIG. 7, the server 160 may continue to evaluatewhether photo-based estimation is appropriate during the method 700 ofFIG. 7. For example, as images are received and a damage quantumindicator determined, the server 160 may compare the damage quantumindicator to a threshold and, if the damage quantum indicator exceedsthe threshold, it may end photo-based estimation and engage the manualestimation module.

Referring now to FIG. 8, a method 800 of enabling image capture isillustrated. The method 800 may be performed by the remote computingdevice 100 in conjunction with the server 160. For example,computer-executable instructions stored in memory of the remotecomputing device 100 may, when executed by a processor of the remotecomputing device 100, configure the remote computing device 100 toperform the method 800 or a portion thereof. The method 800 of FIG. 8 ora portion thereof may be performed, for example, at operation 514 of themethod 500 of FIG. 5.

At operation 810, the remote computing device 100 receives, from acamera associated with the remote computing device, a signal comprisingimage data. The image data represents at least a portion of a vehicle.

Next, at operation 812, the remote computing device 100 retrieves datarepresenting a preferred scene of the vehicle. Retrieving datarepresenting the preferred scene of the vehicle may include obtainingthe preferred scene of the vehicle based on a policy associated with thevehicle. In some embodiments, the data representing the preferred sceneof the vehicle is retrieved from the server 160. For example, the remotecomputing device may send, via the communications module to theclaim-processing server, a signal representing a credential associatedwith a policy. The credential may be one or more of: a token, a policyidentifier, a user name, and/or a password. As described above withreference to operations 712 and 616, the server 160 may use thecredential to identify a profile, account and/or policy. For example,the server may be a claim-processing server and may be configured to usethe credential to identify a type of an insured vehicle and to send thepreferred scene of the vehicle based on the type of the insured vehicle.

The server 160 may then retrieve the data regarding the preferred sceneof the vehicle and send the data regarding the preferred scene of thevehicle to the remote computing device 100 where it is received atoperation 812.

The data representing the preferred scene of the vehicle may be datarepresenting a base vehicle that is of a same type as the vehiclerepresented in the image data. In other embodiments, the datarepresenting the preferred scene of the vehicle may be data representinga generic vehicle that is not of the same type as the vehiclerepresented by the image data. In at least some embodiments, the method800 of FIG. 8 may be performed after the server 160 has received anindication of a location of damage using the techniques described abovewith reference to FIG. 7. In at least some such embodiments, thepreferred scene of the vehicle may be based on the location of damage.For example, as illustrated in FIG. 13, a rear left tire could beselected to indicate that the rear left tire is damages and so theserver 160 may obtain a preferred scene of the vehicle which representsthe rear left tire.

In at least some embodiments, the data representing the preferred sceneof the vehicle may define boundaries for images that are to be captured.

At operation 814, the remote computing device may determine, based onthe image data and based on the data representing the preferred scene ofthe vehicle, whether the received image data corresponds to thepreferred scene of the vehicle. When the received image data correspondsto the preferred scene of the vehicle, the remote computing device mayenable capture of the image data at operation 816.

In some embodiments, to facilitate image capture, the remote computingdevice may display an image representing a desired capture area togetherwith a viewfinder representing the image data received from the camera.For example, the desired capture area may be displayed on a common pageas the viewfinder to allow a user to attempt to use the desired capturearea as a model when framing a photo. In some embodiments, the imagerepresenting the desired capture area may be overlaid on the viewfinder.The overlay may facilitate image capture by allowing the user to attemptto make live camera data align with the desired capture area. In theoverlay, the desired capture area may be displayed as a semi-transparentoverlay so as not to block the live camera data.

In enabling image capture, the remote computing device may enable acamera shutter button to allow the camera shutter button to be selectedto trigger image capture. That is, until the remote computing devicedetermines that the image data corresponds to the preferred scene (atoperation 814), the camera shutter button may be disabled and, inresponse to this determination, the camera shutter button may beenabled.

In some embodiments, in enabling image capture, the remote computingdevice may automatically adjust camera settings. For example, the remotecomputing device may automatically zoom an image and/or mayautomatically focus.

In other embodiments, enabling capture of the image data at operation816 may include updating the graphical user interface to indicate thatimage capture is available. For example, when the image data correspondsto the preferred scene, the GUI may be updated. By way of example, thedisplay screen 1300 of FIG. 13 includes a “go green” function whereby aframe around the viewfinder is updated to turn green when the image datacorresponds to the preferred scene.

In other embodiments, enabling capture of the image data at operation816 may include automatically capturing an image. That is, the remotecomputing device 100 may, upon determining that the image datacorresponds to the preferred scene, automatically capture the image.

When the image does not correspond to a preferred scene, the remotecomputing device 100 may continue to receive image data and evaluatesuch image data until the image data is found to correspond to thepreferred scene.

After an image has been captured, the remote computing device 100 maysend, via the communications module, a signal representing the capturedimage data to a processing server configured to analyze the capturedimage data to assess vehicular damage. That is, the image may be usedfor photo-based estimation as described herein.

Referring now to FIG. 9, a method 900 of providing photo-basedestimation is illustrated. The method 900 may be performed by the server160. For example, computer-executable instructions stored in memory ofthe server 160 may, when executed by a processor of the server 160configure the server 160 to perform the method 900 or a portion thereof.The method 900 of FIG. 9 or a portion thereof may be performed, forexample, at operation 518 of the method 500 of FIG. 5. The method 900may be performed in conjunction with the method 800 of FIG. 8.

At operation 902, the server 160 receives, via the communications modulefrom a remote computing device, a signal representing a credential. Thecredential may be of the type described above and may be received asdescribed above with reference to operation 614 of FIG. 6 or operation710 of FIG. 7. Where the method 900 is performed in conjunction with themethod of FIG. 6, the operations 902 and 614 may be common operationsand where the method 900 is performed in conjunction with the method 700of FIG. 7, the operations 902, 710 may be common operations. Features orfunctions described as being performed at operation 614 and/or 710 maybe performed or provided at operation 902.

Next, at operation 904, the server 160 identifies, based on thecredential, one of the policies stored in a data store. Operation 904may be performed in the manner described above with reference to theoperations 616, 712 of FIGS. 6 and 7 and may be performed together withsuch operations when these methods are performed in conjunction with oneanother. Features or functions described as being performed at operation616 and/or 712 may be performed or provided at operation 904.

Next, at operation 906, the server 160 determines, based on theidentified one of the policies, a vehicle type of an insured vehicle.Then, at operation 908, the server 160 obtains, from the data store andbased on the vehicle type, data representing a preferred scene of avehicle. Then, at operation 910, the server sends, via thecommunications module and to the remote computing device, the datarepresenting the preferred scene of the vehicle and, at operation 912,receives, from the remote computing device, a signal comprising imagedata representing at least a portion of the insured vehicle. Asdescribed above with reference to FIG. 8, the data representing thepreferred scene of the vehicle may be used by the remote computingdevice 100 to enable image capture so that the received image is onethat is known to correspond to the preferred scene of the vehicle. Then,at operation 914, the server 160 may analyze the image data to obtain anindicator. The indicator may numerically quantify an amount of damage tothe insured vehicle. For example, the server 160 may analyze the imagedata to obtain a damage quantum indicator. The damage quantum indicatormay numerically quantify an amount of damage to the insured vehicle. Forexample, the damage quantum indicator may be an estimate of the value ofa repair, replacement or claim.

In determining the indicator, image processing techniques may comparethe vehicle in the image to data representing a base vehicle (i.e., anundamaged vehicle). The data representing the base vehicle may, forexample, including a mapping of parts. For example, the datarepresenting the base vehicle may be an image of a vehicle whichincludes markers or identifiers indicating a part associated withvarious portions of the image. Upon identifying location(s) of thereceived image that do not sufficiently correspond to similar locationsof the base vehicle image, the server 160 may determine that suchlocations represent damaged components and may then identify the damagedcomponents from the mapping of parts. A lookup may then be performed ina database to determine a value associated with the damaged components.

The server 160 may then send (at operation 916), via the communicationsmodule and to the remote computing device, a numerical value based onthe indicator. For example, the server may send, via the communicationsmodule and to the remote computing device, a numerical value based onthe damage quantum indicator. The numerical value may, for example, bean estimate.

Conveniently, by controlling the scene of an image, the server 160 canexpect received images to have desired properties. This may, forexample, simplify the analysis performed by the server 160 on theimages. Thus, by controlling image quality and consistency, thephoto-based estimation may be provided more efficiently and/oraccurately.

Referring now to FIG. 10, a method 1000 of evaluating image data isillustrated. Image data may be evaluated to ensure its authenticity. Forexample, when photo-based estimation is used to automatically evaluate aclaim, there is a risk that images may be tampered with or thatnon-authentic photos may be used. For example, photographic fraud couldinvolve an altered photograph (e.g., a photo altered using photo-editingsoftware, such as Photoshop™), a recycled photograph (e.g., a photo thathas been used for a past claim), a photograph of a vehicle that is notthe insured vehicle, a photograph that was taken a long time ago (e.g.,before the alleged accident), etc.

The method 1000 of FIG. 10 may be used to screen images for at leastsome conditions prior to engaging the photo-based estimation module. Themethod 1000 may be performed by the server 160. For example,computer-executable instructions stored in memory of the server 160 may,when executed by a processor of the server 160, configure the server 160to perform the method 1000 or a portion thereof. The method 1000 of FIG.10 or a portion thereof may be performed, for example, at operation 516of the method 500 of FIG. 5.

At operation 1002, the server 160 receives, via the communicationsmodule from a remote computing device, a signal comprising image dataobtained by the remote computing device through activation of asubmission application. The image data includes an image that may, forexample, be the image that is received at operation 912 of the method900 of FIG. 9 and the operations 1002 and 912 may be common operations.The image data may comprises an image of at least a portion of a vehicleassociated with a claim.

Next, at operation 1004, the server 160 obtains verification data. Theverification data includes at least one of policy data obtained from atleast one of the stored profiles or sensor data received from the remotecomputing device. The verification data is data that is to be used toverify the authenticity of the received image. At operation 1006, theserver 160 evaluates the image data based on the verification data todetermine whether the image data is valid.

At operation 1004, the server 160 may identify the at least one of thestored profiles. The server 160 may identify the stored profile that isassociated with the remote computing device 100 and/or a user of theremote computing device 100. For example, during operation 1004, theserver 160 may receive, via the communications module and from a remotecomputing device, a signal representing identification data. Theidentification data may, for example, be a credential. The credentialmay be one or more of: a token, a PIN, a policy identifier (such as apolicy number), a user name, and/or a password. The credential may bereceived within a chat interface or outside of the chat interface. In anexample in which the credential is received within the chat interface,the chat may prompt a user to provide identifying information such as aname, address, contact information or policy number. In an example inwhich the credential is received outside of the chat interface, asubmission application on the remote computing device may provide atoken to the server 160 or a password to the server. Where the method1000 is performed in conjunction with one or more of the methodsdescribed above, the receipt of the identification data may be performedduring one of the similar operations (e.g., 710, 614, 902) of thosemethods. Features or functions described as being performed at operation614, 710 and/or 902 may be performed or provided at operation 1004.

At operation 1004, the server 160 also identifies, based on theidentification data, one or more of the profiles stored in the datastore. That is, the server 160 identifies the profile that is associatedwith the received identification data. Where the method 1000 isperformed in conjunction with one or more of the methods describedabove, the identification of a profile may be performed during one ofthe similar operations (e.g., 712, 616, 904) of those methods. Once theprofile is identified, data associated with that profile may be used forevaluation purposes (at operation 1006). That is, the server 160 mayevaluate the image data based on the verification data to determinewhether the image data is valid. Features or functions described asbeing performed at operation 616, 712 and/or 904 may be performed orprovided at operation 1004.

Examples of verification data and techniques for evaluating such datawill now be discussed.

The image data that is received at operation 1002 may include an imageof at least a portion of a vehicle and evaluating the image data basedon the verification data may include determining, based on the at leastone of the profiles (e.g., the identified profile), whether the vehiclein the image corresponds to an insured vehicle. If the vehiclerepresented in the image does not correspond to the insured vehicle inthe profile, then this evaluation criteria may be said to have failed.If the vehicle represented in the image corresponds to the insuredvehicle in the profile, then this evaluation criteria may be said to besatisfied.

Various techniques may be employed in order to determine whether thevehicle in the image corresponds to the insured vehicle. For example, inan embodiment, the remote computing device 100 may prompt a user tocapture an image of a vehicle identification number (VIN). The server160 may evaluate such an image by performing optical characterrecognition on the image to identify text in the image and thencomparing the text in the image to a VIN in at least one of the profiles(i.e., in the identified profile). If the purported VIN in the imagematches the VIN in the profile, then this evaluation criteria may besaid to be satisfied, whereas if there is not a match then theevaluation criteria may be said to be failed.

By way of further example, the server 160 may determine whether thevehicle in the image corresponds to the insured vehicle by identifyingone or both of a make or model of the vehicle in the image and comparingone or both of the identified make or model of the vehicle to a make ormodel in the at least one of the profiles (i.e., in the identifiedprofile). The make or model may be identified in the image using varioustechniques. For example, identifying one or both of a make or model ofthe vehicle in the image may include any one or a combination of:identifying a logo in the image and comparing the logo to a logoassociated with the make or model in the at least one of the profiles;identifying text in the image and comparing the text to the make ormodel in the at least one of the profiles; and performing an image-basedanalysis to determine whether a vehicle body of the vehicle in the imagecorresponds to a vehicle body template for the make or model in the atleast one of the profiles.

When the make or model (as the case may be) in the image match the makeor model in the profile, then this evaluation criteria may be said to besatisfied whereas if they do not match this evaluation criteria may besaid to have failed.

By way of further example, the server 160 may determine whether thevehicle in the image corresponds to that in the profile based on a paintcolor. For example, the server 160 may identify a portion of the imagethat represents a body of the vehicle. Boundary identificationtechniques and/or template-based techniques may be used to identify thebody of the vehicle, for example. The server 160 may determine a paintcolour of the vehicle from the identified portion in the image. That is,once the body of the vehicle is identified, then a paint colourassociated with the body of the vehicle may be determined. Then, theserver 160 may determine whether the paint color of the vehiclecorresponds to a paint color specified in the at least one of theprofiles (i.e., the identified profile). When the paint colours match,this evaluation criteria may be said to be satisfied whereas when theydo not this evaluation criteria may be said to have failed.

Evaluation of an image by the server 160 may be performed based onverification data that is sensor data. Where the verification dataincludes sensor data, the sensor data may be data generated by a sensormodule based on a sensed condition associated with the remote computingdevice 100. For example, the sensor data may be associated with alocation subsystem on the remote computing device 100 and may be alocation obtained from the location subsystem. The submissionapplication on the remote computing device 100 which cooperates with theserver 160 may be configured to send the location to the server 160 andit may be received at the server at operation 1004. The location that issent by the remote computing device 100 may be a location indicated bythe location subsystem at the time of image capture on the remotecomputing device 100. While operation 1004 is indicated in FIG. 10 afteroperation 1002, in some embodiments, the order may be reversed and thelocation may be sent by the remote computing device immediately afterimage capture while the image itself might be sent sometime later.

The image represented by the image data may include metadata and themetadata may include a location. The location in the metadata may be alocation at which the remote computing device 100 was located at thetime of image capture and it may have been generated based on thelocation specified by the location subsystem.

Accordingly, the server 160 may receive a location in two differentways: 1) the location may be included in the metadata; and 2) thelocation may be received apart from the metadata. These two locationsshould be reflect a common location. The server 160 may evaluate theimage data by comparing the location obtained from the locationsubsystem (i.e., the location that is received apart from the metadata)to the location included in the metadata provided in the imagerepresented by image data. If the two locations correspond, then thisevaluation criteria may be said to have been satisfied whereas if theydo not then this evaluation criteria may be said to have failed.

A location received from the remote computing device 100 (e.g., alocation in metadata provided in an image represented by the image dataor a location provided by the remote computing device 100 apart from themetadata) may be compared with a location in the at least one of theprofiles (i.e., in the identified profile) to determine whether thelocation received from the remote computing device 100 sufficientlycorresponds to a location associated with the profile. The comparisonmay consider, for example, whether the two locations are within athreshold distance of one another, whether the two locations are in thesame country, whether the two locations are in the same city, whetherthe two locations are in the same province or state, etc. If the twolocations correspond, then this evaluation criteria may be said to havebeen satisfied whereas if they do not then this evaluation criteria maybe said to have failed.

In some embodiments, the image data includes a set of images and server160 may (at operation 1008) evaluate the image data to determine whetherthe set of images is complete and generate an error when the set ofimages is determined to be incomplete. In some embodiments, evaluatingthe image data may include comparing features of an image of the set tofeatures of another image of the set. For example, the images may becompared to determine whether the colour of the vehicle is the same ineach image, whether lighting conditions are the same or similar in eachimage, etc.

In some embodiments, the remote computing device 100 may be configuredto generate a hash chain based on the set of images. The hash chain maybe generated by performing a hash on each image in the set and appendingthe hashes together to form a chain. The hash chain, therefore, includesa hash of a plurality of images in a set and it may be received at theserver 160 prior to receipt of the actual images in the set. The hashchain may be used by the server 160 once the images are received toevaluate the image data by determining whether the set of images in theimage data is a complete set. For example, a hash of each received imagemay be obtained and then the hash chain may be recreated by appendingthe hashes together. The hash chains may be compared to ensure that theycorrespond. If the two hash chains correspond, then this evaluationcriteria may be said to have been satisfied whereas if they do not thenthis evaluation criteria may be said to have failed.

The hash may, in some embodiments, be generated based on some other datainstead of or in addition to the image. For example, location, time,some salt, etc., may be used to generate the hash.

When any evaluation criteria has failed, the image data may bedetermined to be invalid (at operation 1010). Upon determining that theimage data is not valid, the server 160 may generate an error atoperation 1014. If, however, all evaluation criteria is satisfied, thenthe image data may be determined to be valid (at operation 1010) and aphoto-based estimation module may be engaged at operation 1012 (usingtechniques described above). As noted above, the photo-based estimationmodule may be configured to provide a real-time estimate based on theimage data.

In some embodiments, generating an error at operation 1014 may includeengaging a manual estimation module. The manual estimation module may,for example, direct a chat interface displayed on the remote computingdevice to an operator device 150. The operator device allows an operatorto engage in a chat via the chat interface. When the image data isvalid, the server 160 may engage a chat-bot module that is configured togenerate automated replies to input received via a chat interfacedisplayed on the remote computing device.

The server 160 and/or the remote computing device 100 may employ otherverification techniques instead of or in addition to those describedabove. For example, the server 160 may be configured to only acceptimages captured through an associated submission application. By way offurther example, the remote computing device may only allow for “live”captures to be uploaded. That is, it may not permit a saved file to beretrieved and uploaded but may instead require that any upload be basedon a newly-obtained image. In some embodiments, the server 160 and theremote computing device 100 may communicate through encryptedcommunications and the server 160 may only receive images obtainedthrough such encrypted communications. An application that is notconfigured with appropriate keys will be unable to communicate with theserver and, therefore, unable to send images.

In some embodiments, the server 160 may be configured to evaluate atimestamp associated with received image data to determine whether theimage was recently captured. The server 160 may only determine thatimage data is valid if it represents a recently-obtained image. “Recent”may be considered relative to a predetermined threshold.

In some embodiments, the server 160 may be configured to identify anodometer reading in an image and may compare the identified odometerreading to data in an identified profile. For example, mileage may beevaluated relative to known mileage data such as a vehicle's mileage ona previous date, the number of miles typically driven, etc., todetermine whether the odometer reading is valid. By way of example, ifthe odometer reading representing in an image represents a mileage thatis less than a mileage logged in the past and stored in the profile,then the image data may be determined to be invalid and an error may begenerated.

In some instances, there may be a lag between capture of an image andupload of the image to the server. Such a lag may be caused, forexample, because a user may be provided with an opportunity to reviewthe images or because a user may wish to wait to upload the images untilthey are connected to Wi-Fi. The lag between image capture and imageupload may provide an opportunity for tampering in at least somesituations. In some embodiments, in order to prevent tampering, a hash,such as an SHA-256 hash may be performed based on image data immediatelyafter the image is taken and may be sent to the server. Later, after theserver receives the image, the server may use the hash to confirm thatreceived image is the original image. For example, a hash may beperformed based on the received image to ensure that the hash performedon the received image corresponds to the hash performed on the originalimage. Since metadata may be evaluated by the server, the hash may beperformed based on the metadata to ensure that the metadata is nottampered with. The hash may also be performed based on visual data(e.g., the pixels of the image) to ensure that the image itself has notbeen altered.

The methods described above may be modified and/or operations of suchmethods combined to provide other methods.

Furthermore, while the description above generally refers to vehicles,any reference to a vehicle could be replaced with other property.

Furthermore, the description above generally describes operations thatmay be performed by a server and a remote computing device incooperation with one another. Operations that are described as beingperformed by the server may, instead, be performed by the remotecomputing device.

Example embodiments of the present application are not limited to anyparticular operating system, system architecture, mobile devicearchitecture, server architecture, or computer programming language.

It will be understood that the applications, modules, routines,processes, threads, or other software components implementing thedescribed method/process may be realized using standard computerprogramming techniques and languages. The present application is notlimited to particular processors, computer languages, computerprogramming conventions, data structures, or other such implementationdetails. Those skilled in the art will recognize that the describedprocesses may be implemented as a part of computer-executable codestored in volatile or non-volatile memory, as part of anapplication-specific integrated chip (ASIC), etc.

As noted, certain adaptations and modifications of the describedembodiments can be made. Therefore, the above discussed embodiments areconsidered to be illustrative and not restrictive.

What is claimed is:
 1. A server comprising: a communications module; adata store; a processor coupled to the communications module and thedata store; and a memory coupled to the processor, the memory storingprocessor-executable instructions which, when executed by the processor,configure the processor to: send, via the communications module to aremote computing device, a first signal comprising a chat interfaceallowing a user of the remote computing device to send instant textmessages to a chat-bot module during a chat session; receive, via thecommunications module and from the remote computing device, a secondsignal representing one or more instant text messages sent through thechat interface; identify an account associated with the remote computingdevice; retrieve policy data associated with the identified account fromthe data store; engage an artificial intelligence risk detectioncomponent to evaluate the one or more instant text messages and policydata to determine whether a claim has a risk level below a thresholdrisk level, the artificial intelligence risk detection component trainedto detect indicators of risk using previous text messages receivedduring previous chat sessions with other users and risk informationassociated with the previous chat sessions; and responsive todetermining that the claim has the risk level below the threshold risklevel, engage a photo-based estimation module and enable image captureat the remote computing device within the chat interface, thephoto-based estimation module configured to remotely receive a thirdsignal representing image data of a damaged vehicle captured at theremote computing device, automatically analyze the image data to obtainan indicator quantifying an amount of damage to the vehicle and providea real-time estimate within the chat interface based on the indicator.2. The server of claim 1, wherein the instructions further configure theprocessor to: receive a location from the remote computing device, thelocation obtained at the remote computing device from a locationsubsystem; and compare the received location to a location specified inthe policy data.
 3. The server of claim 2, wherein the instructionsfurther configure the processor to: determine that the received locationdoes not correspond to the location specified in the policy data; andwhen it is determined that the received location does not correspond tothe location specified in the policy data, determine that the claim doesnot have the risk level below the risk level threshold.
 4. The server ofclaim 1, wherein the instructions further configure the processor to:determine that a vehicle is expensive to repair by comparing a make ormodel specified in the policy data to a predefined set of makes ormodels in the data store; and when it is determined that the vehicle isexpensive to repair, determine that the claim does not have the risklevel below the risk level threshold.
 5. The server of claim 1, whereinthe indicators of risk include determining that an airbag has beendeployed and the artificial intelligence risk detection componentdetermines that the claim does not have the risk level below the riskthreshold when the one or more instant text messages indicate that theairbag has been deployed.
 6. The server of claim 1, wherein theindicators of risk include determining that a vehicle is not drivableand the artificial intelligence risk detection component determines thatthe claim does not have the risk level below the risk threshold when theone or more instant text messages indicate that the vehicle is notdrivable.
 7. The server of claim 1, wherein engaging a photo-basedestimation module comprises: sending, to the remote computing device viathe communications module, a user interface having a selectable optionto send image data to the server.
 8. The server of claim 1, whereinautomatically analyzing the image data to obtain the indicatorcomprises: identifying a damaged component on the vehicle; andperforming a lookup of the indicator in the data store based on theidentified damaged component.
 9. The server of claim 1, wherein engagingthe photo-based estimation module further comprises: receiving, from theremote computing device, a fourth signal representing further imagedata; automatically analyzing the further image data to update theindicator; and provide an updated real-time estimate within the chatinterface based on the updated indicator.
 10. The server of claim 1,wherein the instructions further configure the processor to: responsiveto determining that the claim does not have the risk level below thethreshold risk level, engage a manual estimation module by handing offthe chat session between the user and the chat-bot to a chat sessionbetween the user and an operator device.
 11. A method comprising:sending, via a communications module to a remote computing device, afirst signal comprising a chat interface allowing a user of the remotecomputing device to send instant text messages to a chat-bot moduleduring a chat session; receiving, via the communications module and fromthe remote computing device, a second signal representing one or moreinstant text messages sent through the chat interface; identifying anaccount associated with the remote computing device; retrieving policydata associated with the identified account from a data store; engagingan artificial intelligence risk detection component to evaluate the oneor more instant text messages and policy data to determine whether aclaim has a risk level below a threshold risk level, the artificialintelligence risk detection component trained to detect indicators ofrisk using previous text messages received during previous chat sessionswith other users and risk information associated with the previous chatsessions; and responsive to determining that the claim has the risklevel below the threshold risk level, engaging a photo-based estimationmodule within the chat interface and enabling image capture at theremote computing device, the photo-based estimation module configured toremotely receive a third signal representing image data of a damagedvehicle captured at the remote computing device, automatically analyzingthe image data to obtain an indicator quantifying an amount of damage tothe vehicle and provide a real-time estimate within the chat interfacebased on the indicator.
 12. The method of claim 11, further comprising:receiving a location from the remote computing device, the locationobtained at the remote computing device from a location subsystem; andcomparing the received location to a location specified in the policydata.
 13. The method of claim 12, further comprising: determining thatthe received location does not correspond to the location specified inthe policy data; and when it is determined that the received locationdoes not correspond to the location specified in the policy data,determining that the claim does not have the risk level below the risklevel threshold.
 14. The method of claim 11, further comprising:determining that a vehicle is expensive to repair by comparing a make ormodel specified in the policy data to a predefined set of makes ormodels in the data store; and when it is determined that the vehicle isexpensive to repair, determining that the claim does not have the risklevel below the risk threshold.
 15. The method of claim 11, wherein theindicators of risk include determining that an airbag has been deployedand the artificial intelligence risk detection component determines thatthe claim does not have the risk level below the risk threshold when theone or more instant text messages indicate that the airbag has beendeployed.
 16. The method of claim 11 wherein the indicators of riskinclude determining that a vehicle is not drivable and the artificialintelligence risk detection component determines that the claim does nothave the risk level below the risk threshold when the one or moreinstant text messages indicate that the vehicle is not drivable.
 17. Themethod of claim 11, wherein engaging the photo-based estimation modulecomprises: sending, to the remote computing device via thecommunications module, a third signal including a user interface havinga selectable option to send image data to a server.
 18. The method ofclaim 11, wherein automatically analyzing the image data to obtain theindicator comprises: identifying a damaged component on the vehicle; andperforming a lookup of the indicator in the data store based on theidentified damaged component.
 19. The method of claim 11, whereinengaging the photo-based estimation module further comprises: receiving,from the remote computing device, a fourth signal representing furtherimage data; automatically analyzing the further image data to update theindicator; and provide an updated real-time estimate within the chatinterface based on the updated indicator.
 20. A non-transitory computerreadable storage medium comprising computer-executable instructionswhich, when executed, configure a computing device to: send, via thecommunications module to a remote computing device, a first signalcomprising a chat interface allowing a user of the remote computingdevice to send instant text messages to a chat-bot module during a chatsession; receive, via the communications module and from the remotecomputing device, a second signal representing one or more instant textmessages sent through the chat interface; identify an account associatedwith the remote computing device; retrieve policy data associated withthe identified account from a data store; engage an artificialintelligence risk detection component to evaluate the one or moreinstant text messages and policy data to determine whether a claim has arisk level below a threshold risk level, the artificial intelligencerisk detection component trained to detect indicators of risk usingprevious text messages received during previous chat sessions with otherusers and risk information associated with the previous chat sessions;and responsive to determining that the claim has the risk level belowthe threshold risk level, engage a photo-based estimation module andenable image capture at the remote computing device within the chatinterface, the photo-based estimation module configured to remotelyreceive a third signal representing image data of a damaged vehiclecaptured at the remote computing device, automatically analyze the imagedata to obtain an indicator quantifying an amount of damage to thevehicle and provide a real-time estimate within the chat interface basedon the indicator.